The usage of plastic cards to carry out monetary transactions is on the rise. Each user carries multiple credit and debit cards to be available handily to carry out the purchase of goods at stores, purchase of commodities over Internet, or to obtain cash from ATMs (Automated Teller Machines). While the credit/debit cards are used for a variety of purposes, the main purpose of cash cards is to be able to withdraw cash from ATMs. These cash cards are associated with bank accounts of the users and help withdraw cash without visiting their banks. Typically, users use ATMs to draw cash to pay for the purchased goods or the obtained services. On account of this, there is a distinct behavioral pattern with respect to cash withdrawal demonstrated by the users. This exhibited pattern is both advantageous and disadvantageous: advantageous as normal transactions can get modeled fairly accurately permitting a somewhat easier way of detecting fraudulent transactions and disadvantageous as fraudsters can observe and mimic this behavior somewhat easily as well. While the typical behavior captures most of the transactions of users, there are, however, atypical transactions at irregular intervals leading to difficulties in modeling these transactions. Further, as compared with the credit card transactions that are rich in variety (and, hence are better modeled), the cash card transactions are very flat. There are systems described in the literature that make an attempt to model both typical and atypical cash card transactions. But because of the wide ranging behavior of users, the prediction accuracy and certainty of the transaction based models get limited.
A powerful approach for dealing with ATM frauds is to rely on biometrics based identification techniques. However, some of the issues related to these identification techniques are: (a) integrating with legacy ATMs consumes time, effort, and money; (b) health and hygiene aspects; and (c) operational costs. It is advantageous if it is possible to model the various characteristics of users and use the same in assessing the normality of the input transactions. A robust solution would involve an integrated approach for modeling both user characteristics and user transaction characteristics. While the literature provides ample examples of modeling latter, a system that effectively models the former enhances the system ability to detect fraudulent transactions.